ArticleApplied mathematics and mechanics2023
Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics.
Article in Applied mathematics and mechanics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Physics-Informed Neural Network-Enabled Forward Prediction and Inverse Design of Ring Origami.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physics-Informed Neural Networks for Modeling Postprandial Plasma Amino Acids Kinetics in Pigs.Animals : an open access journal from MDPI · 2026Article
- One-shot learning for solution operators of partial differential equations.Nature communications · 2025Article
- A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks.Acta biomaterialia · 2025Article
- Scientific Machine Learning for Guided Wave and Surface Acoustic Wave (SAW) Propagation: PgNN, PeNN, PINN, and Neural Operator.Sensors (Basel, Switzerland) · 2025Review
- Article
- Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.Small methods · 2025Article
- Article
- Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta.Nexus (New York, N.Y.) · 2024Article
- Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.Journal of the mechanical behavior of biomedical materials · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.
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